

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      KandinskyVaaControlnetPipeline
   lowerCamelCase__				=      ["image_embeds", "negative_image_embeds", "hint"]
   lowerCamelCase__				=      ["image_embeds", "negative_image_embeds", "hint"]
   lowerCamelCase__				=      [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCamelCase__				=      False





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       return 32





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return 32





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return self.time_input_dim





   @property
   def      _snake_case		(       self					:			Tuple			):
       return self.time_input_dim * 4





   @property
   def      _snake_case		(       self					:			str			):
       return 100





   @property
   def      _snake_case		(       self					:			Any			):
       torch.manual_seed(0			)

       SCREAMING_SNAKE_CASE										=   {
           "in_channels": 8,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image_hint",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       SCREAMING_SNAKE_CASE										=   UNetaDConditionModel(**__lowerCamelCase			)
       return model





   @property
   def      _snake_case		(       self					:			Dict			):
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "AttnDownEncoderBlock2D",
           ],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       torch.manual_seed(0			)
       SCREAMING_SNAKE_CASE										=   VQModel(**self.dummy_movq_kwargs			)
       return model





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.dummy_unet
       SCREAMING_SNAKE_CASE										=   self.dummy_movq

       SCREAMING_SNAKE_CASE										=   DDIMScheduler(
           num_train_timesteps=1000     , beta_schedule="linear"     , beta_start=0.00_085     , beta_end=0.012     , clip_sample=__lowerCamelCase     , set_alpha_to_one=__lowerCamelCase     , steps_offset=1     , prediction_type="epsilon"     , thresholding=__lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }
       return components





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Any     , __lowerCamelCase					:			Any=0			):
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(seed + 1			)			).to(
           __lowerCamelCase			)

       # create hint
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, 3, 64, 64)     , rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

       if str(__lowerCamelCase			).startswith("mps"			):
           SCREAMING_SNAKE_CASE										=   torch.manual_seed(__lowerCamelCase			)
       else:
           SCREAMING_SNAKE_CASE										=   torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "hint": hint,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   "cpu"

       SCREAMING_SNAKE_CASE										=   self.get_dummy_components()

       SCREAMING_SNAKE_CASE										=   self.pipeline_class(**__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   pipe.to(__lowerCamelCase			)

       pipe.set_progress_bar_config(disable=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   output.images

       SCREAMING_SNAKE_CASE										=   pipe(
           **self.get_dummy_inputs(__lowerCamelCase			)     , return_dict=__lowerCamelCase     , )[0]

       SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE										=   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE										=   np.array(
           [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]			)

       assert (
           np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"

       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
       ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"





@slow
@require_torch_gpu
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			int			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"			)

       SCREAMING_SNAKE_CASE										=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/hint_image_cat.png"			)
       SCREAMING_SNAKE_CASE										=   torch.from_numpy(np.array(__lowerCamelCase			)			).float() / 255.0
       SCREAMING_SNAKE_CASE										=   hint.permute(2     , 0     , 1			).unsqueeze(0			)

       SCREAMING_SNAKE_CASE										=   KandinskyVaaPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"     , torch_dtype=torch.floataa			)
       pipe_prior.to(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   KandinskyVaaControlnetPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-controlnet-depth"     , torch_dtype=torch.floataa			)
       SCREAMING_SNAKE_CASE										=   pipeline.to(__lowerCamelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   "A robot, 4k photo"

       SCREAMING_SNAKE_CASE										=   torch.Generator(device="cuda"			).manual_seed(0			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   pipe_prior(
           __lowerCamelCase     , generator=__lowerCamelCase     , num_inference_steps=5     , negative_prompt=""     , ).to_tuple()

       SCREAMING_SNAKE_CASE										=   torch.Generator(device="cuda"			).manual_seed(0			)
       SCREAMING_SNAKE_CASE										=   pipeline(
           image_embeds=__lowerCamelCase     , negative_image_embeds=__lowerCamelCase     , hint=__lowerCamelCase     , generator=__lowerCamelCase     , num_inference_steps=100     , output_type="np"     , )

       SCREAMING_SNAKE_CASE										=   output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(__lowerCamelCase     , __lowerCamelCase			)







'''simple docstring'''







import argparse
import os
import re


__lowerCAmelCase        =							"src/diffusers"

# Pattern that looks at the indentation in a line.
__lowerCAmelCase        =							re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCAmelCase        =							re.compile(R"\[([^\]]+)\]")


def       __UpperCamelCase					(      lowercase_       :  Any   ):
       """simple docstring"""




       a_						     =			_re_indent.search(lowercase_   )
       return "" if search is None else search.groups()[0]


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  int=""   ,       lowercase_       :  Union[str, Any]=None   ,       lowercase_       :  str=None   ):
       """simple docstring"""




       a_						     =			0
       a_						     =			code.split('\n'   )
       if start_prompt is not None:
              while not lines[index].startswith(lowercase_   ):
                     index += 1
              a_						     =			['\n'.join(lines[:index]   )]
       else:
              a_						     =			[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_						     =			[lines[index]]
       index += 1
       while index < len(lowercase_   ) and (end_prompt is None or not lines[index].startswith(lowercase_   )):
              if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
                     if len(lowercase_   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + ' '   ):
                            current_block.append(lines[index]   )
                            blocks.append('\n'.join(lowercase_   )   )
                            if index < len(lowercase_   ) - 1:
                                   a_						     =			[lines[index + 1]]
                                   index += 1
                            else:
                                   a_						     =			[]
                     else:
                            blocks.append('\n'.join(lowercase_   )   )
                            a_						     =			[lines[index]]
              else:
                     current_block.append(lines[index]   )
              index += 1

       # Adds current block if it's nonempty.
       if len(lowercase_   ) > 0:
              blocks.append('\n'.join(lowercase_   )   )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(lowercase_   ):
              blocks.append('\n'.join(lines[index:]   )   )

       return blocks


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       def _inner(lowercase_       :  Union[str, Any]   ):
              return key(lowercase_   ).lower().replace('_'   ,       ''   )

       return _inner


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int=None   ):
       """simple docstring"""




       def noop(lowercase_       :  str   ):
              return x

       if key is None:
              a_						     =			noop
       # Constants are all uppercase, they go first.
       a_						     =			[obj for obj in objects if key(lowercase_   ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_						     =			[obj for obj in objects if key(lowercase_   )[0].isupper() and not key(lowercase_   ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_						     =			[obj for obj in objects if not key(lowercase_   )[0].isupper()]

       a_						     =			ignore_underscore(lowercase_   )
       return sorted(lowercase_   ,       key=lowercase_   ) + sorted(lowercase_   ,       key=lowercase_   ) + sorted(lowercase_   ,       key=lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       def _replace(lowercase_       :  List[Any]   ):
              a_						     =			match.groups()[0]
              if "," not in imports:
                     return F'[{imports}]'
              a_						     =			[part.strip().replace('"'   ,       ''   ) for part in imports.split(','   )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]   ) == 0:
                     a_						     =			keys[:-1]
              return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_   )]   ) + "]"

       a_						     =			import_statement.split('\n'   )
       if len(lowercase_   ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_						     =			2 if lines[1].strip() == '[' else 1
              a_						     =			[(i, _re_strip_line.search(lowercase_   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
              a_						     =			sort_objects(lowercase_   ,       key=lambda lowercase_   : x[1]   )
              a_						     =			[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
       elif len(lowercase_   ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]   ) is not None:
                     a_						     =			_re_bracket_content.sub(_replace   ,       lines[1]   )
              else:
                     a_						     =			[part.strip().replace('"'   ,       ''   ) for part in lines[1].split(','   )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]   ) == 0:
                            a_						     =			keys[:-1]
                     a_						     =			get_indent(lines[1]   ) + ', '.join([F'"{k}"' for k in sort_objects(lowercase_   )]   )
              return "\n".join(lowercase_   )
       else:
              # Finally we have to deal with imports fitting on one line
              a_						     =			_re_bracket_content.sub(_replace   ,       lowercase_   )
              return import_statement


def       __UpperCamelCase					(      lowercase_       :  Dict   ,       lowercase_       :  List[Any]=True   ):
       """simple docstring"""




       with open(lowercase_   ,       'r'   ) as f:
              a_						     =			f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_						     =			split_code_in_indented_blocks(
           lowercase_   ,       start_prompt='_import_structure = {'   ,       end_prompt='if TYPE_CHECKING:'   )

       # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1   ,       len(lowercase_   ) - 1   ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_						     =			main_blocks[block_idx]
              a_						     =			block.split('\n'   )

              # Get to the start of the imports.
              a_						     =			0
              while line_idx < len(lowercase_   ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_						     =			len(lowercase_   )
                     else:
                            line_idx += 1
              if line_idx >= len(lowercase_   ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_						     =			'\n'.join(block_lines[line_idx:-1]   )
              a_						     =			get_indent(block_lines[1]   )
              # Slit the internal block into blocks of indent level 1.
              a_						     =			split_code_in_indented_blocks(lowercase_   ,       indent_level=lowercase_   )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_						     =			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_						     =			[(pattern.search(lowercase_   ).groups()[0] if pattern.search(lowercase_   ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_						     =			[(i, key) for i, key in enumerate(lowercase_   ) if key is not None]
              a_						     =			[x[0] for x in sorted(lowercase_   ,       key=lambda lowercase_   : x[1]   )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_						     =			0
              a_						     =			[]
              for i in range(len(lowercase_   )   ):
                     if keys[i] is None:
                            reordered_blocks.append(internal_blocks[i]   )
                     else:
                            a_						     =			sort_objects_in_import(internal_blocks[sorted_indices[count]]   )
                            reordered_blocks.append(lowercase_   )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_						     =			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]   )

       if code != "\n".join(lowercase_   ):
              if check_only:
                     return True
              else:
                     print(F'Overwriting {file}.'   )
                     with open(lowercase_   ,       'w'   ) as f:
                            f.write('\n'.join(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int=True   ):
       """simple docstring"""




       a_						     =			[]
       for root, _, files in os.walk(lowercase_   ):
              if "__init__.py" in files:
                     a_						     =			sort_imports(os.path.join(lowercase_   ,       '__init__.py'   )   ,       check_only=lowercase_   )
                     if result:
                            a_						     =			[os.path.join(lowercase_   ,       '__init__.py'   )]
       if len(lowercase_   ) > 0:
              raise ValueError(F'Would overwrite {len(lowercase_   )} files, run `make style`.'   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
  __lowerCAmelCase        =							parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)




